Machine-Learning & Data-driven identification of variants to improve neonatal care and patient outcomes

Summary

This project exploits the population data held in the National Neonatal Research Database (NNRD), to develop and apply machine-learning (ML) techniques for the systematic identification of unwarranted variation in a set of clinical outcomes, and their principal care-related determinants.

This is the first time ML tools are applied to the NNRD cohort.

Our initial application has focused on discovery of feeding patterns for very pre-term babies and their association with hazard ratios on outcomes such as mortality, length of stay or breast-milk feeding at discharge.

 

Contact Group member(s):

Sam Greenbury, Jinyi Wu, Elsa Angelini

 


 

Contact Group member(s):

Paul Blakeley